Method for automatically generating fluorescein angiography images based on noninvasive fundus images

Abstract

A method for automatically generating fluorescein angiography images based on noninvasive fundus images, using noninvasive fundus images and matched multi-timepoint fluorescein angiography images as training data to train and generate a conditional generative adversarial network, and to construct the fluorescein angiography images based on the noninvasive fundus images. After the fluorescein angiography images are constructed, it can take noninvasive fundus images as input to generate corresponding early, middle, and late phase fluorescein angiography images. The generated fluorescein angiography images can clearly display the retinal structure and the fluorescence characteristics of various lesions. The method can reduce the reliance on fluorescein angiography, an invasive diagnostic technology with significant risk of side effects, and enhance the ability to diagnose eye diseases.

What is claimed is: 
     
         1 . A method for automatically generating fluorescein angiography images based on noninvasive fundus images, comprising steps as follows:
 S 1 : collecting the noninvasive fundus images and fluorescein angiography images at different angiography periods for an eye, ensuring that retinal structures of the noninvasive fundus images and retinal structures of the fluorescein angiography images are in eye-to-eye correspondence; and   S 2 : using fluorescein angiography images as a gold standard, training a conditional generative adversarial network, inputting the noninvasive fundus images, and then constructing the fluorescein angiography images based on the noninvasive fundus images.   
     
     
         2 . The method as claimed in  claim 1 , wherein the noninvasive fundus images in the step S 1  comprise a regular view noninvasive fundus image to an ultra-wide field fundus image, and the fluorescein angiography images comprise a retinal vascular fluorescein angiography image and a choroidal vascular fluorescein angiography image. 
     
     
         3 . The method as claimed in  claim 1 , wherein the conditional generative adversarial network in the step S 2  comprise a generator and a discriminator;
 the step S 2  further comprises:
 inputting the noninvasive fundus images, and using the generator to generate the fluorescein angiography images; and 
 using real fluorescein angiography images as the gold standard, distinguishing difference between the generated fluorescein angiography images and the gold standard by the discriminator, thereby creating feedback between the generator and the discriminator to continuously update network parameters of the generator until a best game balance is achieved, meaning that the outputted generated fluorescein angiography images are closest to the real fluorescein angiography images; at this point, extracting the generator for use when the model is constructed.